CN115545507A - Indoor space thermal comfort evaluation method, device and system - Google Patents
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Abstract
An indoor space thermal comfort evaluation method, device and system relate to the field of building evaluation. Aiming at the defects of few consideration factors, inadequacy, complex operation and large error of the existing evaluation of the indoor environment thermal comfort level, the factors of age, sex, myoelectricity activity, working state, walking state and aggregation degree are added, and the evaluation cannot be applied to the actual problem due to the complex calculation, the technical scheme provided by the invention is as follows: the indoor space thermal comfort evaluation method comprises the following steps: establishing an influence factor evaluation set influencing the thermal comfort of the indoor space; judging and weighting the importance degree of the influence factors; acquiring and preprocessing influence factor data; analyzing the preprocessed data, and dividing the data into 7 clusters to obtain a thermal comfort evaluation model; carrying out cluster analysis on the acquired actual data by substituting the actual data into a model; and comparing the analysis result with the scale to obtain a comfort evaluation result. The method is suitable for evaluating the indoor thermal comfort level of the building, and is particularly suitable for office places.
Description
Technical Field
Relate to building evaluation field, concretely relates to evaluation of space comfort level in building.
Background
With the continuous improvement of living standard, the requirements of people on the quality of the surrounding environment are also continuously improved, the indoor environment is the most frequently-contacted environment in life, work and study of people and becomes the object of key attention of people, and particularly, the indoor thermal comfort environment has higher requirements. The good indoor thermal environment can enable people to have a pleasant mental state, can concentrate attention and put into learning work more quickly, and improves work efficiency; the uncomfortable indoor heat environment can cause the mood of people to be irritated and even affect the physical and mental health. Therefore, how to quickly and accurately make objective evaluation on indoor thermal comfort plays an important role in timely regulation and control of indoor environment.
The existing evaluation on the thermal comfort level of the indoor environment is characterized in that frequently acquired data are main influence factors such as air temperature and humidity, air flow rate, average radiation temperature, clothing thermal resistance and metabolism rate, the consideration factors are few, numerous experiments show that the factors such as physiological states such as age, gender and brain activity, human activity state, aggregation degree and the like influence the thermal comfort level of a human body, and the factors are taken into an evaluation system to be considered so as to obtain a more accurate thermal comfort level evaluation value. The conventional evaluation model which is usually adopted has the defects of being not objective enough, complex in operation, large in error and the like, and the factors are added into the conventional evaluation model, so that the conventional evaluation model cannot be applied to practice due to the complex calculation.
Disclosure of Invention
Aiming at the problems that the existing indoor environment thermal comfort evaluation is carried out, the frequently collected data are main influence factors such as air temperature and humidity, air flow rate, average radiation temperature, clothing thermal resistance and metabolic rate, the consideration factors are few, the commonly adopted traditional evaluation model has the defects of being not objective enough, complex in operation and large in error, the factors of age, sex, myoelectricity activity, working state, walking state and aggregation degree are added, and the calculation is complex and cannot be applied to practice, the technical scheme provided by the invention is as follows:
the indoor space thermal comfort evaluation method comprises the following steps:
step 1: establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and 2, step: judging and weighting the importance degree of the comfort factor;
and 3, step 3: collecting and preprocessing influencing factors;
and 4, step 4: analyzing the influence factors, and dividing the influence factors into 7 clusters which respectively correspond to 7 ASHRAE scales;
and 5: analyzing the collected actual factors to obtain an analysis result;
step 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
Further, there is provided a preferred embodiment, in the step 1, the comfort factor includes: physical environment, physiological index, activity status and aggregation level;
the physical environment includes: season;
the physiological indexes include: the clothing state, age, sex, skin temperature and myoelectric activity of the user;
the degree of aggregation includes: user working status and ambulatory status.
Further, in a preferred embodiment, in step 3, the influencing factors include: physical environment data, carbon dioxide concentration, dust concentration, user heart rate, and degree of aggregation.
Further, a preferred embodiment is provided, and in the step 2, the judging mode is: the page rank method is adopted.
Further, a preferred embodiment is provided, and in the step 3, the pretreatment mode is: the MAD method is adopted.
Further, a preferred embodiment is provided, and in the steps 4 and 5, the analysis mode is as follows: the k-means + + method was used.
Based on the same inventive concept, the invention also provides an indoor space thermal comfort evaluation device, which comprises:
module 1: the method comprises the following steps of establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and (3) module 2: the system is used for judging and weighting the importance degree of the comfort factor;
and a module 3: the system is used for collecting and preprocessing influencing factors;
and a module 4: the system is used for analyzing the influence factors and dividing the influence factors into 7 clusters which respectively correspond to 7 scales;
and a module 5: the system is used for analyzing the acquired actual factors to obtain an analysis result;
and a module 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
Based on the same inventive concept, the invention also provides an indoor space thermal comfort evaluation system, which is based on the indoor space thermal comfort evaluation method, and the system comprises: the processor is used for collecting the influence factors and the actual factors, and the bracelet is used for collecting user information and uploading the information to the processor.
Based on the same inventive concept, the invention further provides a computer storage medium for storing a computer program, wherein when the computer program stored in the storage medium is read by a processor of a computer, the computer executes the indoor space thermal comfort evaluation method.
Based on the same inventive concept, the invention further provides a computer, which comprises a processor and a storage medium, wherein the storage medium is used for storing a computer program, and when the processor reads the computer program stored in the storage medium, the computer executes the indoor space thermal comfort evaluation method.
Compared with the prior art, the invention has the advantages that:
according to the indoor space thermal comfort evaluation method, the K-means + + algorithm is used for processing data, complex calculation programs are reduced, evaluation results can be calculated quickly and efficiently, meanwhile, data processing is more objective, and the accuracy of the evaluation results is improved.
According to the indoor space thermal comfort evaluation method provided by the invention, the thermal comfort evaluation can be quickly and accurately obtained by simplifying complicated calculation steps, and the method plays a positive role in real-time optimization of indoor thermal comfort and energy consumption control.
According to the indoor space thermal comfort evaluation method provided by the invention, the page rank algorithm is adopted to weight the influence factors, so that the influence of the secondary factors on the result is reduced, and the evaluation result is more objective; besides collecting common influencing factors, the influence of age, sex, myoelectric activity, working state, walking state and aggregation degree on thermal comfort is fully considered; the time and the density of crowd gathering are more accurately measured by using a UWB positioning device; preprocessing data by using an MAD algorithm to remove interference of outliers to a clustering effect; and analyzing the preprocessed data by using a K-means + + algorithm to form a training set, and reducing the influence of initial centroid selection on a clustering result.
The method is suitable for evaluating the indoor thermal comfort level of the building, and is particularly suitable for office places.
Drawings
Fig. 1 is a schematic flow chart of a method for evaluating thermal comfort of an indoor space according to a first embodiment;
FIG. 2 is a diagram illustrating the classification of the factors affecting the indoor thermal comfort according to the second embodiment;
FIG. 3 is a schematic flow chart of the page rank algorithm for weighting the factors according to the fourth embodiment;
fig. 4 is a schematic flowchart of preprocessing the data by the MAD algorithm according to the fifth embodiment;
fig. 5 is a schematic flow chart of the k-means + + algorithm according to the sixth embodiment for processing the data.
Detailed Description
In order to make the advantages and benefits of the technical solutions provided by the present invention more apparent, the technical solutions provided by the present invention will be described in further detail with reference to the accompanying drawings, specifically:
first embodiment, the present embodiment is described with reference to fig. 1, and the present embodiment provides a method for evaluating indoor thermal comfort, the method including:
step 1: establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and 2, step: judging and weighting the importance degree of the comfort factor;
and 3, step 3: collecting and preprocessing influence factors;
and 4, step 4: analyzing the influence factors, and dividing the influence factors into 7 clusters which respectively correspond to 7 ASHRAE scales;
and 5: analyzing the collected actual factors to obtain an analysis result;
step 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
Specifically, the step 2 specifically comprises:
s21, acquiring the number of driven factors and the factor number of each factor according to the relationship among the comfort factors;
s22, dividing the total driving influence factor sum by the driven number by 100% for each factor, and distributing the result to the current factor as the driving influence factor;
s23, constructing an A multiplied by A driving influence factor matrix M, wherein n2 columns in the n1 th row are driving influence factors for driving the n2 th factor by the n1 st factor; n is the total number of factors; the value ranges of n1 and n2 are [1, N ];
s24 sets a factor importance level value PR = [ PR (1), PR (2),..., PR (a) ], where PR (a) is an importance level value of the a-th factor; the value range of a is [1, N ]; the initial value of the importance degree value is [1/N, 1/N.. 1/N ];
s25, performing iterative optimization on the importance degree values of the factors according to the following formula:
obtaining an importance degree value PR of the factor n after the iterative optimization k (n); wherein k is the number of iterations; a is the probability of returning to the previous node, and takes the value of [0, 1%](ii) a M (M, n) is the driving influence factor of the mth row and nth column in the driving influence factor matrix M; PR 0 (m) is the initial importance value of factor m; PR k-1 (m) is the importance degree value of the factor m before the iteration;
s26, taking the value of N from 1 to N, obtaining the importance degree values of all factors after the iterative optimization, taking the importance degree values as new importance degree values, and adding 1 to k each time the iteration is completed;
s27, carrying out iterative optimization for multiple times according to the method until the importance degree values of all the factors are converged, stopping iteration, and weighting each factor according to a certain proportion according to the final importance degree value;
the step 6 specifically comprises:
and (4) processing and analyzing the data, then classifying the data into a certain cluster, and judging the ASHRAE standard corresponding to the cluster to obtain a predicted value of the indoor thermal comfort of the user.
The ASHRAE standard therein is the ASHRAE seven point scale for thermal comfort.
In a second embodiment, the present embodiment is described with reference to fig. 2, and the present embodiment further defines the method for evaluating the thermal comfort level of an indoor space according to the first embodiment, wherein in the step 1, the comfort factor includes: physical environment, physiological index, activity status and aggregation level;
the physical environment includes: season;
the physiological indicators include: the clothing state, age, sex, skin temperature and myoelectric activity of the user;
the degree of aggregation includes: user work status and ambulatory status.
Specifically, the step 1 specifically comprises the following steps:
establishing a comfort factor evaluation set which is a factor influencing indoor heat comfort, wherein the comfort factor comprises a physical environment, a physiological index, an activity state and an aggregation degree;
the physical environment includes: a. the temperature of the air; b. relative humidity; c. an air flow rate; d. an average radiation temperature; f. an air component; g. season;
the physiological indexes include: a. a state of clothing; b. the rate of metabolism; c. the degree of brain activity; d. age; e. sex; f. skin temperature; g. heart rate variability; h. myoelectrical activity;
the active state includes: a. the user does not work in a sitting state; b. carrying out certain work in a sitting posture; c. standing and not working; d. standing for certain work; e. moving at a slow speed; f. moving rapidly;
the degree of aggregation includes: a. the number of people in the space; b. population density in space; c. the size of the space; d. aggregation time.
In a third aspect of the present invention, there is provided the method for evaluating a thermal comfort level of an indoor space according to the first aspect, wherein in the step 3, the influencing factors include: physical environment data, carbon dioxide concentration, dust concentration, user heart rate, and degree of aggregation.
Fourth embodiment, the present embodiment is described with reference to fig. 3, and the present embodiment further limits the method for evaluating indoor thermal comfort according to any one of the first to third embodiments, and the determination in the step 2 is performed in such a manner that: the page rank method is adopted.
The page rank algorithm is adopted to weight the influence factors, so that the influence of secondary factors on the result is reduced, and the evaluation result is more objective;
fifth embodiment, the present embodiment is described with reference to fig. 4, and the present embodiment is further limited to the method for evaluating indoor-space thermal comfort level according to any one of the first to third embodiments, and the preprocessing in step 3 is performed by: the MAD method is adopted.
Specifically, step 3 specifically comprises:
physical environment data acquisition: arranging a temperature measuring instrument, a humidity measuring instrument, an anemometer and a black ball thermometer at multiple points in a building room, and collecting relevant data of a physical environment, including air temperature, air humidity, air flow rate and black ball temperature; carbon dioxide detector for determining CO in different areas in spatially-variable areas 2 Concentration, namely measuring the dust concentration in the air by using a dust detector; inputting season data to an equipment terminal;
acquiring physiological index data: acquiring a thermal image of a user by using a thermal imager, and corresponding the color, the temperature and the clothing amount to obtain the actual clothing amount of the user; placing the acquisition end of the heart rate sensor at a position where blood vessels on the wrist of a user are dense, and acquiring the real-time heart rate of the user; the user acquires the electroencephalogram signals by using the head-mounted equipment, and the brain activity degree is obtained through analysis; a user inputs information such as resting heart rate, age, sex and the like to the processing terminal through equipment such as a bracelet and the like; measuring the hand temperature of a user by using infrared temperature measuring equipment; testing myoelectric activity of a user by using a surface myoelectric tester;
acquiring activity state data: testing the plantar pressure of a user by using a gait analyzer, judging the motion state of the user according to the pressure, and judging whether the user performs certain work or not according to the brain wave activity of the user;
collecting aggregation degree data: a user wears a UWB positioning bracelet and obtains real-time position information by using a bilateral two-way ranging method; measuring and calculating the size of a room; calculating the number of people, crowd gathering density and other related information through the number of UWB positioning points in the space;
the operation flow of acquiring the actual clothing amount of the user by using the thermal imager is as follows:
(1) Corresponding different colors presented by the thermal imager to temperature intervals, wherein the different temperature intervals correspond to clothing amount;
(2) Acquiring a thermal image of a human body by using a thermal imager, and identifying the area of the human body;
(3) Analyzing the distribution of color blocks of the human body thermal image, and converting the color of the color blocks, the temperature and the clothing amount one by one to obtain the actual clothing amount of the human body;
the operation flow of acquiring the activity state of the user by using the gait analyzer and the brain wave tracker is as follows:
(1) The user uses a wearable gait analyzer and a head-mounted brain wave tracker;
(2) Acquiring motion state data of a user through a gait analyzer, and acquiring brain activity data of the user through a brain wave tracker;
(3) Analyzing data such as sole pressure, stride, step frequency, step speed and the like of a user to obtain the motion state (sitting, standing and walking) of the user and whether certain manual labor is carried out, and analyzing electroencephalogram data of the user to determine whether certain mental labor is carried out by the user;
(4) Comprehensively processing the two parts of data to obtain the real-time activity state of the user;
the operation flow of using the UWB positioning device to acquire the degree of aggregation of users is as follows:
(1) A positioning base station is arranged in a building room, and a user wears a UWB positioning bracelet to acquire data;
(2) The UWB positioning hand ring worn by a user sends data to the base station, simultaneously records a sending time stamp, the base station records a receiving time stamp after receiving the data, the base station sends the data to the bracelet after delaying for a period of time, simultaneously records the sending time stamp, the bracelet records the receiving time stamp after receiving the data, the bracelet sends the data to the base station again after delaying for a period of time, simultaneously records the sending time stamp, and the base station records the receiving time stamp after receiving the data;
(3) The time of flight is calculated as follows
Wherein
For data time of flight, T round1 And T reply2 Time difference of bracelet, T reply1 And T round2 Is the time difference of the base station;
(4) Measuring and calculating a data flight distance, namely the distance between a base station and a bracelet according to the data flight time, and measuring and calculating the three-dimensional position of a user in the space through the distance between the bracelet and different base stations;
the user data measured in S31 form a sample data set X = { X = { X = 1 ,X 2 ,......,X n Obtaining Median (X) of the data set through calculation;
s32, calculating absolute deviation | X between each data in each sample set and the median i -Median (X) |, forming a new data set X b ；
S33, calculating to obtain a new data set X b Median of (2) Median (X) b ) The absolute median difference value of the original sample data set X is obtained;
s34 the calculation formula of the absolute median potential is as follows:
MAD＝Median(|X i -Median(X)|)；
s35, performing outlier judgment on the data in the sample data set, wherein the formula is as follows:
Median(X)±nMAD；
for data X in X set i Determining a parameter n, comparing the data in the X set with a formula result, and regarding the data outside the range as an abnormal value;
s36, data X in the sample data set i The processing formula of (1) is as follows:
if the data X i If the average value is smaller than the Median (X) -nMAP, the abnormal value is judged, and the data is adjusted to X i '＝Median(X)-nMAD；
If the data X i Within the range of Median (X) + -nMAG, judging the value to be a normal value and keeping the value unchanged;
if data isX i If the sum is larger than the Median (X) + nMAG, the abnormal value is judged, and the data is adjusted to X i '＝Median(X)+nMAD；
In addition to collecting common influencing factors, the influence of air components and brain activity degree on thermal comfort is considered; the time and the density of crowd gathering are more accurately measured by using a UWB positioning device; preprocessing data by using an MAD algorithm to remove interference of outliers to a clustering effect;
sixth embodiment, the present embodiment is described with reference to fig. 5, and the present embodiment further defines the method for evaluating indoor space thermal comfort provided in any one of the first to third embodiments, and in the steps 4 and 5, the analysis method is as follows: the k-means + + method was used.
Specifically, step 4 specifically includes:
s41, setting a k value to be 7, and analyzing by using a k-means + + algorithm to obtain seven clusters which respectively correspond to seven thermal sensation indexes of cold, slightly cold, neutral, slightly warm, warm and hot of an ASHRAE standard;
s42, randomly selecting a sample from all user data sets as an initial clustering center c 1 ；
S43, calculating the distance between each sample in the data set and the initial clustering center, and selecting the shortest distance D (X) from the distances i )；
S44, selecting the sample with the maximum distance as a new clustering center according to the probability, wherein the probability calculation formula is as follows:
s45, repeating the steps S42 and S43 until all 7 clustering centers are determined;
s46, calculating each sample X and a clustering center c i The formula is as follows:
classifying the samples into the class corresponding to the clustering center with the minimum distance, wherein q is the attribute number of the samples;
s47 recalculates its centroid for each class J:
calculating the mean value of all samples in k clusters, and taking the obtained mean value as a new clustering center, wherein J k Represents the kth class cluster, | J k L represents the number of samples in the kth class cluster, and the summation refers to the class cluster J k The sum of all elements in (A) over each column of attributes, C k Is also a vector containing q attributes;
s48, repeating the step S45 and the step S46 until the center of each cluster is not changed any more, and obtaining a final cluster;
the fifth step is specifically as follows:
s51, processing the data to be predicted by using an MAD algorithm, and eliminating the interference of outliers on clustering results;
s52, substituting the processed data set into the clustering result obtained in the step S4 by using a k-means + + algorithm for clustering analysis;
s6, judging the scale corresponding to the cluster to which the prediction data belongs to obtain a thermal comfort prediction result;
and analyzing the preprocessed data by using a K-means + + algorithm to form a training set, and reducing the influence of initial centroid selection on a clustering result.
Seventh, this embodiment provides an indoor space thermal comfort evaluation device, comprising:
module 1: the method comprises the following steps of establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and (3) module 2: the system is used for judging and weighting the importance degree of the comfort factor;
and a module 3: the system is used for collecting and preprocessing influencing factors;
and (4) module: the system is used for analyzing the influence factors, and is divided into 7 clusters which respectively correspond to 7 scales;
and a module 5: the system is used for analyzing the acquired actual factors to obtain an analysis result;
and a module 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
An eighth embodiment provides an indoor space thermal comfort evaluation system based on the indoor space thermal comfort evaluation method provided in any one of the first to third embodiments, the system including: the processor is used for collecting the influence factors and the actual factors, and the bracelet is used for collecting user information and uploading the information to the processor.
Ninth embodiment provides a computer storage medium storing a computer program, wherein when the computer program stored in the storage medium is read by a processor of a computer, the computer executes the method for evaluating the thermal comfort level of an indoor space according to any one of the first to third embodiments.
Tenth embodiment provides a computer including a processor and a storage medium, wherein the storage medium is used for storing a computer program, and when the processor reads the computer program stored in the storage medium, the computer executes the indoor space thermal comfort evaluation method provided by any one of the first to third embodiments.
The technical solutions provided by the present invention are further described in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solutions provided by the present invention, however, the several specific embodiments described above are only used for explaining the technical solutions provided by the present invention, and are not used for limiting the present invention, and any reasonable modifications and improvements of the present invention, reasonable combinations of the embodiments, equivalent replacements, etc. within the spirit and principle scope of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The indoor space thermal comfort evaluation method is characterized by comprising the following steps:
step 1: establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and 2, step: judging and weighting the importance degree of the comfort factor;
and step 3: collecting and preprocessing influencing factors;
and 4, step 4: analyzing the influence factors, and dividing the influence factors into 7 clusters which respectively correspond to 7 ASHRAE scales;
and 5: analyzing the collected actual factors to obtain an analysis result;
step 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
2. The method for evaluating the thermal comfort level of an indoor space according to claim 1, wherein in the step 1, the comfort factors include: physical environment, physiological index, activity status and aggregation level;
the physical environment includes: season;
the physiological indicators include: the clothing state, age, sex, skin temperature and myoelectrical activity of the user;
the degree of aggregation includes: user working status and ambulatory status.
3. The method for evaluating the thermal comfort level of an indoor space according to claim 1, wherein in the step 3, the influencing factors include: physical environment data, carbon dioxide concentration, dust concentration, user heart rate, and degree of aggregation.
4. An indoor thermal comfort evaluation method according to any one of claims 1 to 3, wherein in the step 2, the judgment manner is as follows: the page rank method is adopted.
5. A method for evaluating a thermal comfort level of an indoor space according to any one of claims 1 to 3, wherein in the step 3, the preprocessing is performed by: the MAD method is adopted.
6. An indoor space thermal comfort evaluation method according to any one of claims 1-3, characterized in that in the steps 4 and 5, the analysis mode is as follows: the k-means + + method was used.
7. Indoor space thermal comfort evaluation device, its characterized in that, the device includes:
module 1: the method is used for establishing a comfort factor evaluation set influencing the thermal comfort of the indoor space;
and a module 2: the system is used for judging and weighting the importance degree of the comfort factor;
and a module 3: the system is used for collecting and preprocessing influencing factors;
and a module 4: the system is used for analyzing the influence factors, and is divided into 7 clusters which respectively correspond to 7 scales;
and a module 5: the system is used for analyzing the acquired actual factors to obtain an analysis result;
and a module 6: and comparing the analysis result with the scale to obtain a comfort evaluation result.
8. Indoor thermal comfort evaluation system, characterized in that the system is based on the indoor thermal comfort evaluation method of any one of claims 1 to 3, the system comprising: the processor is used for collecting the influence factors and the actual factors, and the bracelet is used for collecting user information and uploading the information to the processor.
9. Computer storage medium for storing a computer program, wherein the computer executes the method for evaluating the thermal comfort level of an indoor space according to any one of claims 1 to 3 when the computer program stored in the storage medium is read by a processor of the computer.
10. Computer comprising a processor and a storage medium for storing a computer program, wherein the computer performs the method for evaluating the thermal comfort of an indoor space according to any one of claims 1 to 3 when the processor reads the computer program stored in the storage medium.
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